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import matplotlib.pyplot as plt
import geopandas as gpd

# Create a simple plot for South Korea using its geographic coordinates.
# The coordinates represent the approximate bounding box of South Korea.
minx, miny, maxx, maxy = 124, 33, 132, 39

# Create a simple plot
fig, ax = plt.subplots(1, figsize=(10, 15))
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)

# Add some features to represent key cities
cities = {
    'Seoul': (126.9780, 37.5665),
    'Busan': (129.0756, 35.1796),
    'Incheon': (126.7052, 37.4563),
    'Daegu': (128.6017, 35.8714),
    'Daejeon': (127.3845, 36.3504),
    'Gwangju': (126.8526, 35.1595),
    'Jeju': (126.5312, 33.4996)
}

# Annotate cities
for city, (x, y) in cities.items():
    ax.scatter(x, y, c='red', s=50, zorder=5)
    ax.text(x + 0.1, y, city, fontsize=12)

# Set title and labels
ax.set_title('Approximate Map of South Korea')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

plt.show()

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import zipfile
import os

# Define the path to the uploaded ZIP file and the extraction directory
zip_file_path = '/mnt/data/gadm36_KOR_shp.zip'
extract_dir = '/mnt/data/gadm36_KOR_shp/'

# Create the extraction directory if it doesn't exist
os.makedirs(extract_dir, exist_ok=True)

# Unzip the file
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

# List the contents of the extraction directory
extracted_files = os.listdir(extract_dir)
extracted_files

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# Load the Level 2 shapefile for South Korea
level_2_shapefile_path = os.path.join(extract_dir, 'gadm36_KOR_2.shp')
gdf_level_2 = gpd.read_file(level_2_shapefile_path)

# Plot the Level 2 map of South Korea
fig, ax = plt.subplots(1, figsize=(10, 15))
gdf_level_2.boundary.plot(ax=ax, linewidth=1)
ax.set_title('Level 2 Administrative Divisions of South Korea')
plt.show()

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# Plot the Level 2 map of South Korea again
fig, ax = plt.subplots(1, figsize=(10, 15))
gdf_level_2.boundary.plot(ax=ax, linewidth=1)
ax.set_title('Level 2 Administrative Divisions of South Korea')
plt.show()

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# Plot the Level 2 map of South Korea with thinner boundary lines
fig, ax = plt.subplots(1, figsize=(10, 15))
gdf_level_2.boundary.plot(ax=ax, linewidth=0.1)  # Set the line width to 1/10 of the original
ax.set_title('Level 2 Administrative Divisions of South Korea (Thinner Borders)')
plt.show()

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# Calculate the latitude and longitude range of the Level 2 map of South Korea
minx, miny, maxx, maxy = gdf_level_2.total_bounds

minx, miny, maxx, maxy

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# Define the path to the uploaded ZIP file and the extraction directory for bus stops data
bus_stops_zip_path = '/mnt/data/국토교통부_전국 버스정류장 위치정보_20221012.zip'
bus_stops_extract_dir = '/mnt/data/bus_stops_data/'

# Create the extraction directory if it doesn't exist
os.makedirs(bus_stops_extract_dir, exist_ok=True)

# Unzip the file
with zipfile.ZipFile(bus_stops_zip_path, 'r') as zip_ref:
    zip_ref.extractall(bus_stops_extract_dir)

# List the contents of the extraction directory
bus_stops_extracted_files = os.listdir(bus_stops_extract_dir)
bus_stops_extracted_files

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import pandas as pd

# Since the file names are garbled, let's use the first file assuming it's the CSV file.
csv_file_path = os.path.join(bus_stops_extract_dir, bus_stops_extracted_files[0])

# Try reading a few rows to check the structure of the CSV file
try:
    sample_data = pd.read_csv(csv_file_path, encoding='utf-8', nrows=5)
except:
    try:
        sample_data = pd.read_csv(csv_file_path, encoding='cp949', nrows=5)
    except Exception as e:
        sample_data = f"Error: {e}"

sample_data

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# Read the entire CSV file to get latitude and longitude information of bus stops
try:
    bus_stops_data = pd.read_csv(csv_file_path, encoding='utf-8', usecols=['위도', '경도'])
except:
    bus_stops_data = pd.read_csv(csv_file_path, encoding='cp949', usecols=['위도', '경도'])

# Show the number of rows and first few records
num_rows = len(bus_stops_data)
bus_stops_data.head(), num_rows

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# Plot the Level 2 map of South Korea with bus stops
fig, ax = plt.subplots(1, figsize=(10, 15))

# Plot the administrative borders with thinner lines
gdf_level_2.boundary.plot(ax=ax, linewidth=0.1)

# Plot the bus stops
ax.scatter(bus_stops_data['경도'], bus_stops_data['위도'], c='black', s=0.01, alpha=0.1)

# Set title
ax.set_title('Level 2 Administrative Divisions of South Korea with Bus Stops')

plt.show()

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# Plot the Level 2 map of South Korea with bus stops and adjusted latitude and longitude range
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range
ax.set_xlim(125.08, 130.94)
ax.set_ylim(33.11, 38.61)

# Plot the administrative borders with thinner lines
gdf_level_2.boundary.plot(ax=ax, linewidth=0.1)

# Plot the bus stops
ax.scatter(bus_stops_data['경도'], bus_stops_data['위도'], c='black', s=0.01, alpha=0.1)

# Set title
ax.set_title('Level 2 Administrative Divisions of South Korea with Bus Stops (Adjusted Range)')

plt.show()

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# Plot the Level 2 map of South Korea with bus stops and adjusted opacity for the points
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range
ax.set_xlim(125.08, 130.94)
ax.set_ylim(33.11, 38.61)

# Plot the administrative borders with thinner lines
gdf_level_2.boundary.plot(ax=ax, linewidth=0.1)

# Plot the bus stops with 70% opacity
ax.scatter(bus_stops_data['경도'], bus_stops_data['위도'], c='black', s=0.01, alpha=0.7)

# Set title
ax.set_title('Level 2 Administrative Divisions of South Korea with Bus Stops (70% Opacity Points)')

plt.show()

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# Plot the Level 2 map of South Korea with bus stops, adjusted opacity, and no axis labels or grid
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range
ax.set_xlim(125.08, 130.94)
ax.set_ylim(33.11, 38.61)

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders with thinner lines
gdf_level_2.boundary.plot(ax=ax, linewidth=0.1)

# Plot the bus stops with 70% opacity
ax.scatter(bus_stops_data['경도'], bus_stops_data['위도'], c='black', s=0.01, alpha=0.7)

# Set title
ax.set_title('Level 2 Administrative Divisions of South Korea with Bus Stops (No Axis Labels or Grid)')

plt.show()

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# Filter the GeoDataFrame to only include rows where the region is Seoul
gdf_seoul = gdf_level_2[gdf_level_2['NL_NAME_1'] == 'Seoul']

# Filter the bus stops data to only include rows within the latitude and longitude range of Seoul
seoul_bounds = gdf_seoul.total_bounds
bus_stops_seoul = bus_stops_data[(bus_stops_data['위도'] >= seoul_bounds[1]) & (bus_stops_data['위도'] <= seoul_bounds[3]) &
                                 (bus_stops_data['경도'] >= seoul_bounds[0]) & (bus_stops_data['경도'] <= seoul_bounds[2])]

# Plot the map focused on Seoul
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range based on Seoul's boundaries
ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders of Seoul with thinner lines
gdf_seoul.boundary.plot(ax=ax, linewidth=0.1)

# Plot the bus stops in Seoul with 70% opacity
ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=0.7)

# Set title
ax.set_title('Administrative Divisions of Seoul with Bus Stops (No Axis Labels or Grid)')

plt.show()

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# Check if 'Seoul' exists in the 'NL_NAME_1' column or identify the column containing city names
column_with_city_names = None
for column in gdf_level_2.columns:
    if 'Seoul' in gdf_level_2[column].values:
        column_with_city_names = column
        break

# If we've identified the correct column, let's proceed to filter the data.
if column_with_city_names:
    gdf_seoul = gdf_level_2[gdf_level_2[column_with_city_names] == 'Seoul']
    seoul_bounds = gdf_seoul.total_bounds
    bus_stops_seoul = bus_stops_data[
        (bus_stops_data['위도'] >= seoul_bounds[1]) & (bus_stops_data['위도'] <= seoul_bounds[3]) &
        (bus_stops_data['경도'] >= seoul_bounds[0]) & (bus_stops_data['경도'] <= seoul_bounds[2])
    ]
    
    # Plot the map focused on Seoul
    fig, ax = plt.subplots(1, figsize=(10, 15))

    # Set the latitude and longitude range based on Seoul's boundaries
    ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
    ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

    # Hide latitude and longitude labels
    ax.set_xticks([])
    ax.set_yticks([])

    # Hide grid
    ax.grid(False)

    # Plot the administrative borders of Seoul with thinner lines
    gdf_seoul.boundary.plot(ax=ax, linewidth=0.1)

    # Plot the bus stops in Seoul with 70% opacity
    ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=0.7)

    # Set title
    ax.set_title('Administrative Divisions of Seoul with Bus Stops (No Axis Labels or Grid)')

    plt.show()
else:
    print("Could not find column containing city names with 'Seoul'.")

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# Plot the map focused on Seoul with thicker border lines
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range based on Seoul's boundaries
ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders of Seoul with thicker lines (10 times thicker)
gdf_seoul.boundary.plot(ax=ax, linewidth=1)  # 10 times the previous thickness of 0.1

# Plot the bus stops in Seoul with 70% opacity
ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=0.7)

# Set title
ax.set_title('Administrative Divisions of Seoul with Bus Stops (Thicker Borders, No Axis Labels or Grid)')

plt.show()

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from matplotlib.font_manager import FontProperties

# Load the custom font
font_path = '/mnt/data/NotoSansKR-Bold.ttf'
font_properties = FontProperties(fname=font_path)

# Plot the map focused on Seoul with thicker border lines and updated points
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range based on Seoul's boundaries
ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders of Seoul with thicker lines (10 times thicker)
gdf_seoul.boundary.plot(ax=ax, linewidth=1)

# Plot the bus stops in Seoul with 100% opacity
ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=1.0)

# Add names of the administrative divisions using the NotoSansKR-Bold font
for x, y, label in zip(gdf_seoul.geometry.centroid.x, gdf_seoul.geometry.centroid.y, gdf_seoul['NL_NAME_2']):
    ax.text(x, y, label, fontsize=8, fontproperties=font_properties, ha='center', va='center')

# Set title using the NotoSansKR-Bold font
ax.set_title('Administrative Divisions of Seoul with Bus Stops (Thicker Borders, No Axis Labels or Grid)', 
             fontproperties=font_properties, fontsize=14)

plt.show()

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# Count the number of bus stops in each administrative division of Seoul
bus_stops_seoul_gdf = gpd.GeoDataFrame(bus_stops_seoul, geometry=gpd.points_from_xy(bus_stops_seoul['경도'], bus_stops_seoul['위도']))
bus_stops_seoul_gdf.crs = gdf_seoul.crs
bus_stops_counts = gpd.sjoin(bus_stops_seoul_gdf, gdf_seoul, op='within')['index_right'].value_counts().reset_index()
bus_stops_counts.columns = ['index_right', 'bus_stops_count']

# Merge the counts back into the Seoul GeoDataFrame
gdf_seoul_with_counts = gdf_seoul.reset_index().merge(bus_stops_counts, on='index_right', how='left').fillna(0)

# Plot the map focused on Seoul with bus stop counts
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range based on Seoul's boundaries
ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders of Seoul with thicker lines
gdf_seoul.boundary.plot(ax=ax, linewidth=1)

# Plot the bus stops in Seoul with 100% opacity
ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=1.0)

# Add names of the administrative divisions and bus stop counts
for x, y, label, count in zip(gdf_seoul_with_counts.geometry.centroid.x, gdf_seoul_with_counts.geometry.centroid.y, 
                              gdf_seoul_with_counts['NL_NAME_2'], gdf_seoul_with_counts['bus_stops_count']):
    ax.text(x, y, f"{label} ({int(count)})", fontsize=8, fontproperties=font_properties, ha='center', va='center')

# Set title
ax.set_title('Administrative Divisions of Seoul with Bus Stops and Counts (Thicker Borders, No Axis Labels or Grid)', 
             fontproperties=font_properties, fontsize=14)

plt.show()

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# Manually count the number of bus stops in each administrative division of Seoul
bus_stops_counts_manual = {}

for index, row in gdf_seoul.iterrows():
    polygon = row['geometry']
    count = bus_stops_seoul_gdf.within(polygon).sum()
    bus_stops_counts_manual[row['NL_NAME_2']] = count

# Convert the manual counts to a DataFrame for easier merging
bus_stops_counts_df = pd.DataFrame(list(bus_stops_counts_manual.items()), columns=['NL_NAME_2', 'bus_stops_count'])

# Merge the counts back into the Seoul GeoDataFrame
gdf_seoul_with_counts = gdf_seoul.merge(bus_stops_counts_df, on='NL_NAME_2', how='left').fillna(0)

# Plot the map focused on Seoul with bus stop counts
fig, ax = plt.subplots(1, figsize=(10, 15))

# Set the latitude and longitude range based on Seoul's boundaries
ax.set_xlim(seoul_bounds[0], seoul_bounds[2])
ax.set_ylim(seoul_bounds[1], seoul_bounds[3])

# Hide latitude and longitude labels
ax.set_xticks([])
ax.set_yticks([])

# Hide grid
ax.grid(False)

# Plot the administrative borders of Seoul with thicker lines
gdf_seoul.boundary.plot(ax=ax, linewidth=1)

# Plot the bus stops in Seoul with 100% opacity
ax.scatter(bus_stops_seoul['경도'], bus_stops_seoul['위도'], c='black', s=0.01, alpha=1.0)

# Add names of the administrative divisions and bus stop counts
for x, y, label, count in zip(gdf_seoul_with_counts.geometry.centroid.x, gdf_seoul_with_counts.geometry.centroid.y, 
                              gdf_seoul_with_counts['NL_NAME_2'], gdf_seoul_with_counts['bus_stops_count']):
    ax.text(x, y, f"{label} ({int(count)})", fontsize=8, fontproperties=font_properties, ha='center', va='center')

# Set title
ax.set_title('Administrative Divisions of Seoul with Bus Stops and Counts (Thicker Borders, No Axis Labels or Grid)', 
             fontproperties=font_properties, fontsize=14)

plt.show()

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# Create a pie chart for the number of bus stops in each administrative division of Seoul
fig, ax = plt.subplots(1, figsize=(10, 10))

# Data to plot
labels = gdf_seoul_with_counts['NL_NAME_2']
sizes = gdf_seoul_with_counts['bus_stops_count']
colors = plt.cm.Paired(np.arange(len(labels)))

# Plot the pie chart
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 10, 'fontproperties': font_properties})

# Set title
ax.set_title('Distribution of Bus Stops in Seoul by Administrative Division', fontproperties=font_properties, fontsize=14)

plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

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import numpy as np

# Create a pie chart for the number of bus stops in each administrative division of Seoul
fig, ax = plt.subplots(1, figsize=(10, 10))

# Data to plot
labels = gdf_seoul_with_counts['NL_NAME_2']
sizes = gdf_seoul_with_counts['bus_stops_count']
colors = plt.cm.Paired(np.arange(len(labels)))

# Plot the pie chart
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 10, 'fontproperties': font_properties})

# Set title
ax.set_title('Distribution of Bus Stops in Seoul by Administrative Division', fontproperties=font_properties, fontsize=14)

plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

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# Create a pie chart with more varied colors using the 'tab20' colormap
fig, ax = plt.subplots(1, figsize=(10, 10))

# Data to plot
labels = gdf_seoul_with_counts['NL_NAME_2']
sizes = gdf_seoul_with_counts['bus_stops_count']
colors = plt.cm.tab20(np.linspace(0, 1, len(labels)))

# Plot the pie chart
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 10, 'fontproperties': font_properties})

# Set title
ax.set_title('Distribution of Bus Stops in Seoul by Administrative Division', fontproperties=font_properties, fontsize=14)

plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

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# Create a pie chart with more varied colors and updated labels
fig, ax = plt.subplots(1, figsize=(10, 10))

# Update labels to only include the name before the '|' character
updated_labels = [label.split('|')[0] for label in gdf_seoul_with_counts['NL_NAME_2']]

# Data to plot
sizes = gdf_seoul_with_counts['bus_stops_count']

# Use a colormap with more distinct colors and avoid using the same color for adjacent slices
colors = plt.cm.nipy_spectral(np.linspace(0, 1, len(updated_labels)))

# Shuffle the colors to avoid similar colors being adjacent
np.random.shuffle(colors)

# Plot the pie chart
ax.pie(sizes, labels=updated_labels, colors=colors, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 10, 'fontproperties': font_properties})

# Set title
ax.set_title('Distribution of Bus Stops in Seoul by Administrative Division', fontproperties=font_properties, fontsize=14)

plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

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# Sort the data by 'bus_stops_count' in descending order for better visualization in the pie chart
sorted_gdf = gdf_seoul_with_counts.sort_values(by='bus_stops_count', ascending=False)

# Update labels to only include the name before the '|' character and sort them based on 'bus_stops_count'
sorted_labels = [label.split('|')[0] for label in sorted_gdf['NL_NAME_2']]

# Data to plot
sorted_sizes = sorted_gdf['bus_stops_count']

# Use a colormap with more distinct colors and avoid using the same color for adjacent slices
sorted_colors = plt.cm.nipy_spectral(np.linspace(0, 1, len(sorted_labels)))

# Shuffle the colors to avoid similar colors being adjacent
np.random.shuffle(sorted_colors)

# Plot the pie chart with sorted data
fig, ax = plt.subplots(1, figsize=(10, 10))
ax.pie(sorted_sizes, labels=sorted_labels, colors=sorted_colors, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 10, 'fontproperties': font_properties})

# Set title
ax.set_title('Distribution of Bus Stops in Seoul by Administrative Division (Sorted)', fontproperties=font_properties, fontsize=14)

plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()